Why Companies Prefer to Use Python with Hadoop?

Hadoop framework is written in Java language, but it is entirely possible for Hadoop programs to be coded in Python or C++ language. Which implies that data architects don’t have to learn Java, if they are familiar with Python. World of analytics don’t have many Java programmers (lovers!), so Python comes across as one of the most user friendly, easy to learn, flexible language and yet extremely powerful for end-to-end advanced analytics applications. We can write programs like MapReduce in Python language, without the need for translating the code into Java jar files. First order of business is to check out the Python frameworks available for working with Hadoop:

Hadoop Streaming API

Dumbo

Mrjob

Pydoop

Hadoopy

Before we explore industry use cases where Python is used with Hadoop, let’s make a distinction between these two technologies. Hadoop is a database framework, which allows users to save, process Big Data in a fault tolerant, low latency ecosystem using programming models. However Hadoop has recently developed into an ecosystem of technologies and tool to complement Big Data processing.

On the other hand, Python is a programming language and it has nothing to do with the Hadoop ecosystem. Python is an object oriented language, similar to C++ or Java, but is used for a variety of applications like web development, advanced analytics, artificial intelligence, natural language processing, etc. Python is a flexible language with abundance of resources and libraries; and it concentrates on code productivity and readability.

With a choice between programming languages like Java, Scala and Python for Hadoop ecosystem, most developers use Python because of its supporting libraries for data analytics tasks. Majority of companies nowadays prefer their employees to be proficient In Python, because of the versatility of the language’s application; and they use Hadoop Streaming API (preferably for text processing) along with other such frameworks to deal with Big Data problems using Python language. Hadoop Streaming API is a utility which goes along with Hadoop Distribution. Hadoop streaming allows user to create and execute Map/Reduce jobs with any script or executable as the mapper or/and the reducer.

In this article, we have highlighted several examples of how tech companies are using Hadoop with Python.

Facebook Face Finder Application

Facebook is leading research and development in the discipline of image processing and it processes huge amounts of Image based unstructured data. Facebook enables HDFS to store and extract this enormous data, and it uses Python as the backend language for most of its Image Processing applications such as Image resizing, facial image extraction, etc.

Therefore Facebook uses Python as a common platform for its image related application and uses Hadoop Streaming API to access and edit the data.

Quora Search Algorithm

Quora manages incredible amount of textual data using Hadoop, Apache Spark and several other data-warehousing technologies. Since Quora’s back end is developed on Python; this language is used to interact with the HDFS. Hence Quora uses Hadoop with Python to extract Question upon search or for suggestion.

Amazon’s Product Recommendation

Amazon has a leading platform which suggests preferable products to existing users based on their search and buying pattern. Their machine learning engine is built using Python and it interacts with their database system, i.e. Hadoop Ecosystem. These two technologies work in coherence to deliver top of the class product recommendation system and fault tolerant database interactions.

Multiple disciplines have inducted the use of python with Hadoop in their application. This is because Python is a popular language with various available features for Big Data Analytics. Python programming language is dynamically typed, extendable, portable and scalable; which makes it a lucrative option for Big Data application based out of Hadoop. Some of the other notable industry use cases for Hadoop with Python are mentioned below:

Limeroad integrated Hadoop, Python and Apache spark to create a realtime recommendation system for its online visitors, using their search pattern.

Images acquired from Hubble Telescope are stored using Hadoop framework and Python is used for image processing on this database.

Youtube’s recommendation engine is also built using Python and Apache Spark for realtime analytics.

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12 thoughts on “Why Companies Prefer to Use Python with Hadoop?”

I do not have any technical experience ( Programming or Language ) . I want to go for Certified Big Data Expert / Data Science using SAS & R course . What are the prerequisites for the course . Do I need to know any particular language .

For this course there is no specific pre-requisite and candidates doesn’t need to be from programming background necessarily. But please share your detailed profile with us on info@analytixlabs.co.in or feel free to call us for more detailed discussion so that we can guide you with suitable course based on overall profile.

Use the Python library bite to access HDFS programmatically from inside Python applications
Write MapReduce jobs in Python with mrjob, the Python MapReduce library
Extend Pig Latin with user-defined functions (UDFs) in Python
Use the Spark Python API (PySpark) to jot down Spark programs with Python
Learn how to use the Luigi Python work flow hardware to manage MapReduce jobs and Pig scripts

I am currently senior system engineer (age 38) working in US at American Airlines as a contractor. I am planning move into hadoop as developer later into Scala. I hear everyone saying you should java to program Mapreduce. But many web discussion say u can program with python too. As a beginner in development should i choose Java or python and also I would like to know what employer or recruiters will expect a programming skill set from my end ?

You are correct. As a developer, you can use Java or Python to write mapreduce programs. Java is more preferable given that entire Hadoop ecosystem is developed on top of Java. If you are planning to move Spark using scala, you can also prefer Java since there are many similarities between Java and Scala. However if you are planning for Hadoop Data Analyst, python is more preferable given that it has many libraries to perform advanced analytics and also you can use Spark to perform advanced analytics and implement machine learning techniques using pyspark API.

To learn Hadoop and build an excellent career in Hadoop, having basic knowledge of Linux and knowing the basic programming principles of Java is a must. Thus, to incredibly excel in the entrenched technology of Apache Hadoop, it is recommended that you at least learn Java basics.